Domain-aware decomposition for supply chain master planning using linear programming

ABSTRACT

A system and method are disclosed for solving a supply chain planning problem modeled as a linear programming (LP) problem. Embodiments include receiving an LP problem representing a supply chain planning problem for a supply chain network comprising material buffers and resource buffers, partitioning the supply chain network at a complicating node into at least two supply chains sharing the complicating node, formulating a decomposed subproblem for each of the supply chains, calculating an effective dual based, at least in part, on a mathematical difference of at least two dual values calculated by solving the functional-based decomposed subproblems, and generating a globally-optimal LP solution to the LP problem using subgradient descent with the effective dual.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present disclosure is related to that disclosed in the U.S.Provisional Application No. 62/856,357, filed Jun. 3, 2019, entitled“Domain-Aware Decomposition for Supply Chain Master Planning usingLinear Programming” and U.S. Provisional Application No. 62/895,872,filed Sep. 4, 2019, entitled “Domain-Aware Decomposition for SupplyChain Master Planning using Linear Programming.” U.S. ProvisionalApplication Nos. 62/856,357 and 62/895,872 are assigned to the assigneeof the present application. The subject matter disclosed in U.S.Provisional Application Nos. 62/856,357 and 62/895,872 are herebyincorporated by reference into the present disclosure as if fully setforth herein. The present invention hereby claims priority under 35U.S.C. § 119(e) to U.S. Provisional Application Nos. 62/856,357 and62/895,872.

TECHNICAL FIELD

The present disclosure relates generally to supply chain planning andspecifically to systems and methods solving of linear programming supplychain planning problems using functional decomposition.

BACKGROUND

During supply chain planning, a supply chain plan may be generated bymodeling and solving a supply chain planning problem as a linearprogramming (LP) problem. Although this approach may generate optimalsolutions, it is overly time consuming, resource intensive, and oftenrequires simplifying constraints or objectives to finish the solvewithin pre-specified batch solve windows. Speeding up solve times cansometimes be accomplished by decomposing a monolithic LP into multiplesmaller problems, which are then solved individually. Unfortunately,monolithic LP problems are generally not amenable to standarddecomposition techniques. The inability to decompose monolithic LPproblems to improve solving speed of supply chain planning problems isundesirable.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the present invention may be derived byreferring to the detailed description when considered in connection withthe following illustrative figures. In the figures, like referencenumbers refer to like elements or acts throughout the figures.

FIG. 1 illustrates a supply chain network, in accordance with a firstembodiment;

FIG. 2 illustrates the supply chain planner of FIG. 1 in greater detail,in accordance with an embodiment;

FIG. 3 illustrates an exemplary simplified supply chain network, inaccordance with an embodiment;

FIG. 4 illustrates an exemplary simplified supply chain network, inaccordance with an embodiment;

FIG. 5 illustrates decomposition of the exemplary simplified supplychain network of FIG. 4 into simplified decomposed supply chains, inaccordance with an embodiment;

FIG. 6 illustrates an exemplary simplified supply chain network, inaccordance with an embodiment;

FIG. 7 illustrates an exemplary supply chain graph representing theexemplary simplified supply chain network of FIG. 6, in accordance withan embodiment;

FIG. 8A illustrates an exemplary simplified convergent supply chainnetwork, in accordance with an embodiment;

FIG. 8B illustrates an exemplary simplified divergent supply chainnetwork, in accordance with an embodiment;

FIG. 8C illustrates an exemplary simplified generic supply chainnetwork, in accordance with an embodiment;

FIG. 9 illustrates a method of solving a supply chain planning problemby functional decomposition, in accordance with an embodiment;

FIG. 10 illustrates a method of supply chain problem partitioning, inaccordance with an embodiment;

FIG. 11A illustrates exemplary code implementing the dividing of aconvergent supply chain at a complicating node, in accordance with anembodiment;

FIG. 11B illustrates exemplary code implementing the dividing of adivergent supply chain at a complicating node, in accordance with anembodiment;

FIG. 11C illustrates exemplary code implementing the dividing of ageneric supply chain at a complicating node, in accordance with anembodiment;

FIG. 12 illustrates a method of solving functional-based decomposedsubproblems using masterless iteration, in accordance with anembodiment; and

FIGS. 13A-13B illustrate a chart and a table comparing run time of an LPoptimization method with the functional decomposition method of FIG. 9,in accordance with an embodiment.

DETAILED DESCRIPTION

Aspects and applications of the invention presented herein are describedbelow in the drawings and detailed description of the invention. Unlessspecifically noted, it is intended that the words and phrases in thespecification and the claims be given their plain, ordinary, andaccustomed meaning to those of ordinary skill in the applicable arts.

In the following description, and for the purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the various aspects of the invention. It will beunderstood, however, by those skilled in the relevant arts, that thepresent invention may be practiced without these specific details. Inother instances, known structures and devices are shown or discussedmore generally in order to avoid obscuring the invention. In many cases,a description of the operation is sufficient to enable one to implementthe various forms of the invention, particularly when the operation isto be implemented in software. It should be noted that there are manydifferent and alternative configurations, devices and technologies towhich the disclosed inventions may be applied. The full scope of theinventions is not limited to the examples that are described below.

FIG. 1 illustrates supply chain network 100, in accordance with a firstembodiment. Supply chain network 100 comprises supply chain planner 110,inventory system 120, transportation network 130, one or more imagingdevices 140, one or more supply chain entities 150, computer 160,network 170, and communication links 180-190. Although a single supplychain planner 110, a single inventory system 120, a singletransportation network 130, one or more imaging devices 140, one or moresupply chain entities 150, a single computer 160, and a single network170 are shown and described, embodiments contemplate any number ofsupply chain planners, inventory systems, transportation networks,imaging devices, supply chain entities, computers, or networks,according to particular needs.

In one embodiment, supply chain planner 110 comprises server 112 anddatabase 114. Server 112 comprises one or more modules that model,decompose, and solve a supply chain planning problem by utilizing thefunctional structure of supply chain network 100 to identify where tosplit the supply chain planning problem into two or more subproblems.One factor that prevents decomposing large and complicated supply chainplanning problems are resource or material constraints that are commonto two or more subproblems. Supply chain planner 110 identifies thesecommon resource and material constraints (referred to herein ascomplicating constraints), replicates the complicating constraintsacross two or more subproblems, calculates an effective dual for thecomplicating constraints, and allocates the resources or materials ofthe complicating constraints using the effective dual. Solver 206 (seeFIG. 2) of server 112 solves the subproblems sequentially, or inparallel, followed by masterless iteration with subgradient descent tocalculate the globally-optimal solution. This approach solveshierarchical optimization problems and calculates the combinedreduced-cost in the presence of smaller subproblems.

Inventory system 120 comprises server 122 and database 124. Server 122of inventory system 120 is configured to receive and transmit productdata 216 (see FIG. 2) (including, for example, item identifiers, pricingdata, and attribute data), inventory data 220 (including, for example,inventory levels), and other like data about one or more items at one ormore locations in supply chain network 100. Server 122 stores andretrieves data about the one or more items from database 124 or from oneor more locations in supply chain network 100.

Transportation network 130 comprises server 132 and database 134.According to embodiments, transportation network 130 directs one or moretransportation vehicles 136 to ship one or more items between one ormore supply chain entities 150, based, at least in part, on the numberof items currently in transit in transportation network 130, a supplychain plan, including a supply chain master plan, the number of itemscurrently in stock at one or more supply chain entities 150, aforecasted demand, a supply chain disruption, a material or capacityreallocation, current and projected inventory levels at one or morestocking locations, and/or one or more additional factors describedherein. One or more transportation vehicles 136 comprise, for example,any number of trucks, cars, vans, boats, airplanes, unmanned aerialvehicles (UAVs), cranes, robotic machinery, or the like. One or moretransportation vehicles 136 may comprise radio, satellite, or othercommunication that communicates location information (such as, forexample, geographic coordinates, distance from a location, globalpositioning satellite (GPS) information, or the like) with supply chainplanner 110, inventory system 120, transportation network 130, one ormore imaging devices 140, and/or one or more supply chain entities 150to identify the location of one or more transportation vehicles 136 andthe location of an item of any inventory or shipment located on one ormore transportation vehicles 136.

One or more imaging devices 140 comprise one or more processors 142,memory 144, one or more sensors 146, and may include any suitable inputdevice, output device, fixed or removable computer-readable storagemedia, or the like. According to embodiments, one or more imagingdevices 140 comprise an electronic device that receives data from one ormore sensors 146. One or more sensors 146 of one or more imaging devices140 may comprise an imaging sensor, such as, a camera, scanner,electronic eye, photodiode, charged coupled device (CCD), or any otherelectronic component that detects visual characteristics (such as color,shape, size, fill level, or the like) of objects. One or more imagingdevices 140 may comprise, for example, a mobile handheld electronicdevice such as, for example, a smartphone, a tablet computer, a wirelesscommunication device, and/or one or more networked electronic devicesconfigured to image items using one or more sensors 146 and transmitproduct images to one or more databases.

In addition, or as an alternative, one or more sensors 146 may comprisea radio receiver and/or transmitter configured to read from and/or writeto an electronic tag, such as, for example, a radio-frequencyidentification (RFID) tag. Each item may be represented in supply chainnetwork 100 by an identifier, including, for example, Stock-Keeping Unit(SKU), Universal Product Code (UPC), serial number, barcode, tag, RFID,or like device that encodes identifying information. One or more imagingdevices 140 may generate a mapping of one or more items in supply chainnetwork 100 by scanning an identifier associated with an item andidentifying the item based, at least in part, on the scan. This mayinclude, for example, a stationary scanner located at one or more supplychain entities 150 that scans items as the items pass near the scanner.As explained in more detail below, supply chain planner 110, inventorysystem 120, transportation network 130, and one or more imaging devices140 may use the mapping of an item to locate the item in supply chainnetwork 100. The location of the item may be used to coordinate thestorage and transportation of items in supply chain network 100according to one or more plans generated by supply chain planner 110and/or a reallocation of materials or capacity. Plans may comprise oneor more of a master supply chain plan, production plan, distributionplan, and the like.

Additionally, one or more sensors 146 of one or more imaging devices 140may be located at one or more locations local to, or remote from, one ormore imaging devices 140, including, for example, one or more sensors146 integrated into one or more imaging devices 140 or one or moresensors 146 remotely located from, but communicatively coupled with, oneor more imaging devices 140. According to some embodiments, one or moresensors 146 may be configured to communicate directly or indirectly withone or more of supply chain planner 110, inventory system 120,transportation network 130, one or more imaging devices 140, one or moresupply chain entities 150, computer 160, and/or network 170 using one ormore communication links 180-190.

As shown in FIG. 1, supply chain network 100 comprising supply chainplanner 110, inventory system 120, transportation network 130, one ormore imaging devices 140, and one or more supply chain entities 150 mayoperate on one or more computers 160 that are integral to or separatefrom the hardware and/or software that support supply chain planner 110,inventory system 120, transportation network 130, one or more imagingdevices 140, and one or more supply chain entities 150. Computer 160 mayinclude any suitable input device 162, such as a keypad, mouse, touchscreen, microphone, or other device to input information. Output device164 may convey information associated with the operation of supply chainnetwork 100, including digital or analog data, visual information, oraudio information.

Computer 160 may include fixed or removable computer-readable storagemedia, including a non-transitory computer-readable medium, magneticcomputer disks, flash drives, CD-ROM, in-memory device or other suitablemedia to receive output from and provide input to supply chain network100. Computer 160 may include one or more processors 166 and associatedmemory to execute instructions and manipulate information according tothe operation of supply chain network 100 and any of the methodsdescribed herein. In addition, or as an alternative, embodimentscontemplate executing the instructions on computer 160 that causecomputer 160 to perform functions of the method. Further examples mayalso include articles of manufacture including tangible non-transitorycomputer-readable media that have computer-readable instructions encodedthereon, and the instructions may comprise instructions to performfunctions of the methods described herein.

Supply chain planner 110, inventory system 120, transportation network130, one or more imaging devices 140, and one or more supply chainentities 150 may each operate on one or more separate computers 160, anetwork of one or more separate or collective computers 160, or mayoperate on one or more shared computers 160. In addition, supply chainnetwork 100 may comprise a cloud-based computing system havingprocessing and storage devices at one or more locations local to, orremote from, supply chain planner 110, inventory system 120,transportation network 130, one or more imaging devices 140, and one ormore supply chain entities 150. In addition, each of one or morecomputers 160 may be a work station, personal computer (PC), networkcomputer, notebook computer, tablet, personal digital assistant (PDA),cell phone, telephone, smartphone, mobile device, wireless data port,augmented or virtual reality headset, or any other suitable computingdevice. In an embodiment, one or more users may be associated withsupply chain planner 110, inventory system 120, transportation network130, one or more imaging devices 140, and one or more supply chainentities 150. These one or more users may include, for example, a“manager” or a “planner” handling supply chain planning and/or one ormore related tasks within supply chain network 100. In addition, or asan alternative, these one or more users within supply chain network 100may include, for example, one or more computers 160 programmed toautonomously handle, among other things, production planning, demandplanning, option planning, sales and operations planning, supply chainmaster planning, plan adjustment after supply chain disruptions, orderplacement, automated warehouse operations (including removing items fromand placing items in inventory), robotic production machinery (includingproducing items), and/or one or more related tasks within supply chainnetwork 100.

One or more supply chain entities 150 may represent one or moresuppliers 152, manufacturers 154, distribution centers 156, andretailers 158 of one or more supply chain networks 100, including one ormore enterprises. One or more suppliers 152 may be any suitable entitythat offers to sell or otherwise provides one or more components to oneor more manufacturers 154. One or more suppliers 152 may, for example,receive a product from a first supply chain entity in supply chainnetwork 100 and provide the product to another supply chain entity. Oneor more suppliers 152 may comprise automated distribution systems 153that automatically transport products to one or more manufacturers 154based, at least in part, on the number of items currently in transit intransportation network 130, a supply chain plan, including a supplychain master plan, the number of items currently in stock at one or moresupply chain entities 150, a forecasted demand, a supply chaindisruption, a material or capacity reallocation, current and projectedinventory levels at one or more stocking locations, and/or one or moreadditional factors described herein.

One or more manufacturers 154 may be any suitable entity thatmanufactures at least one product. One or more manufacturers 154 may useone or more items during the manufacturing process to produce anymanufactured, fabricated, assembled, or otherwise processed item,material, component, good or product. Items may comprise, for example,components, materials, products, parts, supplies, or other items, thatmay be used to produce products. In addition, or as an alternative, anitem may comprise a supply or resource that is used to manufacture theitem, but does not become a part of the item. In one embodiment, aproduct represents an item ready to be supplied to, for example, anothersupply chain entity, an item that needs further processing, or any otheritem. One or more manufacturers 154 may, for example, produce and sell aproduct to one or more suppliers 152, another one or more manufacturers154, one or more distribution centers 156, one or more retailers 158, orany other suitable customer or one or more supply chain entities 150.One or more manufacturers 154 may comprise automated robotic productionmachinery 155 that produce products based, at least in part, on thenumber of items currently in transit in transportation network 130, asupply chain plan, including a supply chain master plan, the number ofitems currently in stock at one or more supply chain entities 150, aforecasted demand, a supply chain disruption, a material or capacityreallocation, current and projected inventory levels at one or morestocking locations, and/or one or more additional factors describedherein.

One or more distribution centers 156 may be any suitable entity thatoffers to sell or otherwise distributes at least one product to one ormore retailers 158, customers, or any suitable one or more supply chainentities 150. One or more distribution centers 156 may, for example,receive a product from a first supply chain entity in supply chainnetwork 100 and store and transport the product for a second supplychain entity. One or more distribution centers 156 may compriseautomated warehousing systems 157 that automatically transport an itemto, remove an item from, or place an item into inventory of one or moreretailers 158, customers, or one or more supply chain entities 150based, at least in part, on the number of items currently in transit intransportation network 130, a supply chain plan, including a supplychain master plan, the number of items currently in stock at one or moresupply chain entities 150, a forecasted demand, a supply chaindisruption, a material or capacity reallocation, current and projectedinventory levels at one or more stocking locations, and/or one or moreadditional factors described herein.

One or more retailers 158 may be any suitable entity that obtains one ormore products to sell to one or more customers. In addition, one or moreretailers 158 may sell, store, and supply one or more components and/orrepair a product with one or more components. One or more retailers 158may comprise any online or brick and mortar location, includinglocations with shelving systems 159. Shelving systems 159 may comprise,for example, various racks, fixtures, brackets, notches, grooves, slots,or other attachment devices for fixing shelves in variousconfigurations. These configurations may comprise shelving withadjustable lengths, heights, and other arrangements, which may beadjusted by an employee of one or more retailers based oncomputer-generated instructions or automatically by machinery to placeproducts in a desired location, and which may be based, at least inpart, on the number of items currently in transit in transportationnetwork 130, a supply chain plan, including a supply chain master plan,the number of items currently in stock at one or more supply chainentities 150, a forecasted demand, a supply chain disruption, a materialor capacity reallocation, current and projected inventory levels at oneor more stocking locations, and/or one or more additional factorsdescribed herein.

Although one or more suppliers 152, manufacturers 154, distributioncenters 156, and retailers 158 are shown and described as separate anddistinct entities, the same entity may simultaneously act as any one ormore suppliers 152, manufacturers 154, distribution centers 156, andretailers 158. For example, one or more manufacturers 154 acting as amanufacturer could produce a product, and the same entity could act asone or more suppliers 158 to supply a product to another one or moresupply chain entities 150. Although one example of supply chain network100 is shown and described, embodiments contemplate any configuration ofsupply chain network 100, without departing from the scope of thepresent disclosure.

In one embodiment, supply chain planner 110 may be coupled with network170 using communication link 180, which may be any wireline, wireless,or other link suitable to support data communications between supplychain planner 110 and network 170 during operation of supply chainnetwork 100. Inventory system 120 may be coupled with network 170 usingcommunication link 182, which may be any wireline, wireless, or otherlink suitable to support data communications between inventory system120 and network 170 during operation of supply chain network 100.Transportation network 130 may be coupled with network 170 usingcommunication link 184, which may be any wireline, wireless, or otherlink suitable to support data communications between transportationnetwork 130 and network 170 during operation of supply chain network100. One or more imaging devices 140 are coupled with network 170 usingcommunication link 186, which may be any wireline, wireless, or otherlink suitable to support data communications between one or more imagingdevices 140 and network 170 during operation of distributed supply chainnetwork 100. One or more supply chain entities 150 may be coupled withnetwork 170 using communication link 188, which may be any wireline,wireless, or other link suitable to support data communications betweenone or more supply chain entities 150 and network 170 during operationof supply chain network 100. Computer 160 may be coupled with network170 using communication link 190, which may be any wireline, wireless,or other link suitable to support data communications between computer160 and network 170 during operation of supply chain network 100.

Although communication links 180-190 are shown as generally couplingsupply chain planner 110, inventory system 120, transportation network130, one or more imaging devices 140, one or more supply chain entities150, and computer 160 to network 170, each of supply chain planner 110,inventory system 120, transportation network 130, one or more imagingdevices 140, one or more supply chain entities 150, and computer 160 maycommunicate directly with each other, according to particular needs.

In another embodiment, network 170 includes the Internet and anyappropriate local area networks (LANs), metropolitan area networks(MANs), or wide area networks (WANs) coupling supply chain planner 110,inventory system 120, transportation network 130, one or more imagingdevices 140, one or more supply chain entities 150, and computer 160.For example, data may be maintained local to, or external of, supplychain planner 110, inventory system 120, transportation network 130, oneor more imaging devices 140, one or more supply chain entities 150, andcomputer 160 and made available to one or more associated users ofsupply chain planner 110, inventory system 120, transportation network130, one or more imaging devices 140, one or more supply chain entities150, and computer 160 using network 170 or in any other appropriatemanner. For example, data may be maintained in a cloud database at oneor more locations external to supply chain planner 110, inventory system120, transportation network 130, one or more imaging devices 140, one ormore supply chain entities 150, and computer 160 and made available toone or more associated users of supply chain planner 110, inventorysystem 120, transportation network 130, one or more imaging devices 140,one or more supply chain entities 150, and computer 160 using the cloudor in any other appropriate manner. Those skilled in the art willrecognize that the complete structure and operation of network 170 andother components within supply chain network 100 are not depicted ordescribed. Embodiments may be employed in conjunction with knowncommunications networks and other components.

In accordance with the principles of embodiments described herein,supply chain planner 110 may generate a supply chain plan, including asupply chain master plan. Furthermore, one or more computers 160associated with supply chain network 100 may instruct automatedmachinery (i.e., robotic warehouse systems, robotic inventory systems,automated guided vehicles, mobile racking units, automated roboticproduction machinery, robotic devices and the like) to adjust productmix ratios, inventory levels at various stocking points, production ofproducts by manufacturing equipment, proportional or alternativesourcing of one or more supply chain entities 150, and the configurationand quantity of packaging and shipping of items based on the number ofitems currently in transit in transportation network 130, a supply chainplan, including a supply chain master plan, a solution to a supply chainplanning problem, the number of items currently in stock at one or moresupply chain entities 150, a forecasted demand, a supply chaindisruption, a material or capacity reallocation, current and projectedinventory levels at one or more stocking locations, and/or one or moreadditional factors described herein. For example, the methods describedherein may include computers 160 receiving product data 216 fromautomated machinery having at least one sensor and product data 216corresponding to an item detected by the automated machinery. Receivedproduct data 216 may include an image of the item, an identifier, asdescribed above, and/or product information associated with the item,including, for example, dimensions, texture, estimated weight, and thelike. Computers 160 may also receive, from one or more sensors 146 ofone or more imaging devices 140, a current location of the identifieditem.

According to embodiments, the methods may further include computers 160looking up received product data 216 in a database 114 to identify theitem corresponding to product data 216 received from automatedmachinery. Based on the identification of the item, computers 160 mayalso identify (or alternatively generate) a first mapping in database114, where the first mapping is associated with the current location ofthe identified item. Computers 160 may also identify a second mapping indatabase 114, where the second mapping is associated with a pastlocation of the identified item. Computers 160 may also compare thefirst mapping and the second mapping to determine when the currentlocation of the identified item in the first mapping is different thanthe past location of the identified item in the second mapping.Computers 160 may send instructions to the automated machinery based, atleast in part, on one or more differences between the first mapping andthe second mapping such as, for example, to locate items to add to, orremove from, an inventory or shipment of one or more supply chainentities 150. In addition, or as an alternative, supply chain planner110 monitors one or more supply chain constraints of one or more itemsat one or more supply chain entities 150 and adjusts the orders and/orinventory of one or more supply chain entities 150 at least partiallybased on one or more detected supply chain constraints.

FIG. 2 illustrates supply chain planner 110 of FIG. 1 in greater detail,in accordance with an embodiment. As discussed above, supply chainplanner 110 comprises server 112 and database 114. Although supply chainplanner 110 is shown as comprising a single server 112 and a singledatabase 114, embodiments contemplate any suitable number of servers ordatabases internal to, or externally coupled with, supply chain planner110.

Server 112 of supply chain planner 110 may comprise modeler 202,decomposition module 204, and solver 206. Although server 112 is shownand described as comprising a single modeler 202, a single decompositionmodule 204, and a single solver 206, embodiments contemplate anysuitable number or combination of these located at one or morelocations, local to, or remote from supply chain planner 110, such as onmultiple servers or computers at any location in supply chain network100.

Modeler 202 may model one or more supply chain planning problems, suchas a master planning problem, for supply chain network 100. In oneembodiment, modeler 202 of server 112 models a supply chain planningproblem as an LP supply chain planning problem. Additionally or as analternative, modeler 202 of server 112 models a supply chain planningproblem as a supply chain network graph. Although modeler 202 isdescribed as modeling supply chain planning problems as LP supply chainplanning problems and supply chain network graphs, embodimentscontemplate modeler 202 generating other mathematical and graphicalmodels of supply chain planning problems, according to particular needs.

Decomposition module 204 traverses supply chain network graphs todetermine the configuration of nodes and edges, identify complicatingconstraints, and decompose the supply chain planning problem intosmaller supply chains. Based on the analysis of the supply chain networkgraph, decomposition module 204 generates at least two decomposedsubproblems from the LP supply chain planning problem.

Solver 206 of server 112 comprises one or more optimization, heuristic,or mathematical solvers that utilize functional decomposition, LPoptimization, and masterless iteration to generate a solution to themaster supply chain planning problem. As described in further detailbelow, solver 206 calculates LP solutions to subproblems representingthe decomposed supply chains using masterless iteration with subgradientdescent to generate globally-optimal LP solution.

Database 114 of supply chain planner 110 may comprise one or moredatabases or other data storage arrangement at one or more locations,local to, or remote from, server 112. Database 114 comprises, forexample, data models, 210 supply chain input data 212, LP formulations214, product data 216, demand data 218, inventory data 220, supply chainmodels 222, and inventory policies 224. Although database 114 is shownand described as comprising data models 210, supply chain input data212, LP formulations 214, product data 216, demand data 218, inventorydata 220, supply chain models 222, and inventory policies 224,embodiments contemplate any suitable number or combination of these,located at one or more locations, local to, or remote from, supply chainplanner 110 according to particular needs.

As an example only and not by way of limitation, database 114 storesdata models 210, which represent the flow of materials through one ormore supply chain entities 150 of supply chain network 100. Modeler 202of supply chain planner 110 may model the flow of materials through oneor more supply chain entities 150 of supply chain network 100 as one ormore data models 210 comprising, for example, a network of nodes andedges. Material storage and/or transition units may be modeled as nodes,which may be referred to as buffer nodes, buffers, or nodes. Each nodemay represent a buffer for an item (such as, for example, a rawmaterial, intermediate good, finished good, component, and the like),resource, or operation (including, for example, a production operation,assembly operation, transportation operation, and the like). Varioustransportation or manufacturing processes are modeled as edgesconnecting the nodes. Each edge may represent the flow, transportation,or assembly of materials (such as items or resources) between the nodesby, for example, production processing or transportation. According tosome embodiments, the quantity of consumption and production isindicated as a weight on an edge. Although the LP supply chain planningproblems are described as comprising supply chain network graphs,embodiments contemplate representing LP supply chain planning problemsusing other graphical models to represent LP supply chain planningproblems, according to particular needs.

Supply chain input data 212 may comprise various decision variables,business constraints, goals, and objectives of one or more supply chainentities 150. According to some embodiments, supply chain input data 212may comprise hierarchical objectives specified by, for example, businessrules, master planning requirements, scheduling constraints, anddiscrete constraints, including, for example, sequence-dependent setuptimes, lot-sizing, storage, shelf life, and the like.

LP formulation 214 of database 114 include a single- or multi-objectiveLP supply chain master planning problems, matrix formulations of the LPsupply chain master planning problem, and decomposed subproblems as wellas any associated data and mappings used to formulate or solve an LPproblem, such as, for example, LP constraint-variable matrix, identityof complicating constraints, decomposed subproblems, globally-optimal LPsolutions, objectives, objective hierarchies, and fixed variables.According to embodiments, LP formulation comprises mathematicalobjective functions that represent business objectives, such as, forexample, minimizing the quantity of unmet demand, minimizing usage ofalternate resources (e.g. maximizing usage of primary resources),planning items as just-in-time (JIT) as possible (e.g. minimizing theamount of carried-over items), and the like. LP formulationsadditionally comprise mathematical constraints representing limitationsto capacity, materials, lead times, and the like; and minimum andmaximum values for decision variables representing lower and upperbounds. By way of example only and not of limitation, the lower andupper bounds for the capacity of a machine may be set at zero hours andten hours, respectively. In this example, zero hours comprises the lowerbound (because a machine cannot be used for a negative period of time)and ten hours represents a maximum number of hours the machine may beused in a day.

According to one embodiments, LP formulation 214 is an LPconstraint-variable matrix, which comprises a sparse matrix havingconstraints expressed by rows, variables represented by columns, andeach element comprising the coefficient of the variable for eachconstraint. As described in further detail below, subproblems created bydecomposing master LP supply chain planning problem are submatrices ofLP constraint variable matrix and comprise constraints expressed byrows, variables represented by columns, and each element comprising thecoefficient of the variable for each constraint. Solver 206 createssubproblems from decomposed master LP supply chain planning problem bydecomposing LP constraint variable matrix according to the entities thatare allocated to each decomposed supply chain. In addition, LPformulation and/or LP constraint-variable matrix may comprise one ormore additional rows or columns in the same matrix, one or more othermatrices, one or more submatrices, and the like, which may store othercomponents associated with LP problem, such as, for example, objectives,right-hand side (RHS) values, lower/upper bounds, and the like,according to particular needs. Although the LP supply chain planningproblems are described as comprising LP constraint variable matrix,embodiments contemplate representing LP supply chain planning problemsusing other mathematical models, according to particular needs.

Product data 216 of database 114 may comprise one or more datastructures for identifying, classifying, and storing data associatedwith products, including, for example, a product identifier (such as aStock Keeping Unit (SKU), Universal Product Code (UPC), or the like),product attributes and attribute values, sourcing information, and thelike. Product data 216 may comprise data about one or more productsorganized and sortable by, for example, product attributes, attributevalues, product identification, sales quantity, demand forecast, or anystored category or dimension. Attributes of one or more products may be,for example, any categorical characteristic or quality of a product, andan attribute value may be a specific value or identity for the one ormore products according to the categorical characteristic or quality,including, for example, physical parameters (such as, for example, size,weight, dimensions, fill level, color, and the like).

Demand data 218 of database 114 may comprise, for example, any datarelating to past sales, past demand, purchase data, promotions, events,or the like of one or more supply chain entities 150. Demand data 218may cover a time interval such as, for example, by the minute, hour,daily, weekly, monthly, quarterly, yearly, or any suitable timeinterval, including substantially in real time. According toembodiments, demand data 218 may include historical demand and salesdata or projected demand forecasts for one or more retail locations,customers, regions, or the like of one or more supply chain entities 150and may include historical or forecast demand and sales segmentedaccording to product attributes, customers, regions, or the like.

Inventory data 220 of database 114 may comprise any data relating tocurrent or projected inventory quantities or states, order rules, or thelike. For example, inventory data 220 may comprise the current level ofinventory for each item at one or more stocking locations across supplychain network 100. In addition, inventory data 220 may comprise orderrules that describe one or more rules or limits on setting an inventorypolicy, including, but not limited to, a minimum order quantity, amaximum order quantity, a discount, a step-size order quantity, andbatch quantity rules. According to some embodiments, supply chainplanner 110 accesses and stores inventory data 220 in database 114,which may be used by supply chain planner 110 to place orders, setinventory levels at one or more stocking points, initiate manufacturingof one or more items (or components of one or more items), or the like.In addition, or as an alternative, inventory data 220 may be updated byreceiving current item quantities, mappings, or locations from inventorysystem 120, transportation network 130, one or more imaging devices 140,and/or one or more supply chain entities 150.

Supply chain models 222 of database 114 may comprise characteristics ofa supply chain setup to deliver the customer expectations of aparticular customer business model. These characteristics may comprisedifferentiating factors, such as, for example, MTO (Make-to-Order), ETO(Engineer-to-Order) or MTS (Make-to-Stock). Additionally, or in thealternative, supply chain models 222 may comprise characteristics thatspecify the supply chain structure in even more detail, including, forexample, specifying the type of collaboration with the customer (e.g.Vendor-Managed Inventory (VMI)), the identity of stocking locations orsuppliers from which items may be sourced, customer priorities, demandpriorities, how products may be allocated, shipped, or paid for, byparticular customers, and the destination stocking locations or one ormore supply chain entities 150 where items may be transported.Differences of these characteristics may lead to different supply chainmodels 222.

Inventory policies 224 of database 114 may comprise any suitableinventory policy describing the reorder point and target quantity, orother inventory policy parameters that set rules for supply chainplanner 110 to manage and reorder inventory. Inventory policies 224 maybe based on target service level, demand, cost, fill rate, or the like.According to embodiment, inventory policies 224 comprise target servicelevels that ensure that a service level of one or more supply chainentities 150 is met with a certain probability. For example, one or moresupply chain entities 150 may set a target service level at 95%, meaningone or more supply chain entities 150 will set the desired inventorystock level at a level that meets demand 95% of the time. Although, aparticular target service level and percentage is described; embodimentscontemplate any target service level, for example, a target servicelevel of approximately 99% through 90%, 75%, or any target servicelevel, according to particular needs. Other types of service levelsassociated with inventory quantity or order quantity may comprise, butare not limited to, a maximum expected backlog and a fulfillment level.Once the service level is set, supply chain planner 110 may determine areplenishment order according to one or more replenishment rules, which,among other things, indicates to one or more supply chain entities 150to supply or receive inventory to replace the depleted inventory.

FIG. 3 illustrates an exemplary simplified supply chain network 300, inaccordance with an embodiment. Exemplary simplified supply chain network300 comprises two or more suppliers 152 a-152 n, two or moremanufacturers 154 a-154 n, four or more distribution centers 156 a-156n, and three or more retailers 158 a-158 n. Two or more manufacturers154 a-154 n receive items from two or more suppliers 152 a-152 n forproduction processes 302 a-302 n. Production processes 302 a-302 ncomprise various operations for processing items, intermediate items,and finished goods, which may comprise one or more products transportedto four or more distribution centers 156 a-156 n. Four or moredistribution centers 156 a-156 n may transport products to three or moreretailers 158 a-158 n. The flow of materials, items, and products amongthe two or more suppliers 152 a-152 n, two or more manufacturers 154a-154 n, four or more distribution centers 156 a-156 n, and three ormore retailers 158 a-158 n of exemplary simplified supply chain network300 must meet demand requirements while being limited by constraints ofcapacity, materials, lead times, transportation, sourcing, and/or thelike. Although a simplified exemplary supply chain network 300 isillustrated as comprising two or more suppliers 152 a-152 n, two or moremanufacturers 154 a-154 n, four or more distribution centers 156 a-156n, and three or more retailers 158 a-158 n, supply chain network 100 maycomprise any number of one or more supply chain entities 150, accordingto particular needs. For example, supply chain network 100 oftencomprises multiple manufacturing plants located in different regions orcountries. In addition, an item may be processed from many materials bymany operations into a large number of different intermediate goodsand/or finished items, where the different operations may have multipleconstrained resources and multiple input items, each with their ownlead, transportation, production, and cycle times. Additionally,materials and resources may flow upstream, downstream, or both, subjectto material and capacity constraints and demand requirements.

FIG. 4 illustrates exemplary simplified supply chain network 400,according to an embodiment. Modeler 202 models simplified supply chainnetwork 400 to generate a supply chain network model representing theflow of items and resources between nodes. In one embodiment, modeler202 creates a supply chain network model representing the flow of itemsand resources between nodes, in accordance with the constraints at eachoperation and buffer. As disclosed above, items may comprise, forexample, components, materials, products, parts, supplies, or otheritems. In one embodiment, items flow from upstream nodes to downstreamnodes along edges from left to right. This flow may represent, forexample, raw materials at upstream nodes being transformed into finishedproducts at downstream nodes. However, flows may be bidirectional, andone or more items may flow from right to left, from a downstream node toan upstream node, according to particular needs. According to oneembodiment, simplified supply chain network 400 comprises four materialbuffers (B1-B4) 402 a-402 d, four operations (O1-O4) 404 a-404 d, and asingle resource buffer (R1,3) 406 connected by edges 408 a-408 h. Firstoperation (O1) 404 a and third operation (O3) 404 c share capacity ofresource buffer (R1,3) 406 Satisfaction of demand 410 a depends on theallocation of capacity of resource buffer (R1,3) 406 to first operation(O1) 404 a, and the satisfaction of demand 410 b depends on theallocation of capacity of resource buffer (R1,3) 406 to third operation(O3) 404 c.

FIG. 5 illustrates decomposition of the exemplary simplified supplychain network 400 of FIG. 4 into simplified decomposed supply chains 502and 504, according to an embodiment. By setting the shared resourcebuffer (R1,3) 406 as the complicating constraint, supply chain planner110 duplicates resource buffer (R1,3) 406 and decomposes exemplarysimplified supply chain network 400 into two supply chains 502 and 504.Each of supply chains 502 and 504 receive one of duplicated resourcebuffers (R1,3) 506 a-506 b. Duplicated resource buffers (R1,3) 506 a-506b receive an allocation of the original capacity of resource buffer(R1,3) 406. In one embodiment, first capacity 508 a allocated to firstduplicated resource buffer (R1,3) 506 a is a percentage (e.g. x %) ofthe total original capacity of resource buffer (R1,3) 406, and secondcapacity 508 b allocated to second duplicated resource buffer (R1,3) 506b is the remaining percentage (e.g. (100−x) %) of the total originalcapacity of resource buffer (R1,3) 406. By way of example only and notof limitation, if the original capacity of resource buffer (R1,3) 406 is300 units and forty percent is allocated to first capacity 508 a offirst duplicated resource buffer (R1,3) 506 a and sixty percent isallocated second capacity 508 b of second duplicated resource buffer(R1,3) 506 b, then first duplicated resource buffer (R1,3) 506 a wouldreceive 120 units of capacity, and second duplicated resource buffer(R1,3) 506 b would receive 180 units. Although the allocation ofcapacity to duplicated resource buffers (R1,3) 506 a-506 b is describedas 40% and 60% of a total original capacity of 300 units, embodimentscontemplate any suitable allocation of any total original capacity toany suitable number of duplicated resource buffers, according toparticular needs. Further, each different allocation of capacity betweenduplicated resource buffers (R1,3) 506 a-506 b will differently affectthe satisfaction of demands 410 a-410 b. An optimal allocation tocapacities 508 a-508 b of duplicated resource buffers (R1,3) 506 a-506 bsatisfies demands 410 a-410 b equal to the quantity of the optimalsupply chain plan.

As described in further detail below, supply chain planner 110 maydecompose a single supply chain network such as, for example, exemplarysimplified supply chain network 400 into models of two supply chainswhich share a common material or resource constraint, such as, forexample, simplified decomposed supply chains 502 and 504, which sharecapacity of resource buffer (R1,3) 406. Supply chain planner 110partitions the supply chain network at one or more nodes (representing acomplicating constraint) and models and formulates LP subproblems foreach of the smaller, divided supply chains. Depending on the location ofthe one or more shared constraints in a supply chain network,decomposition module 204 of supply chain planner 110 may utilize adifferent decomposition process. In one embodiment, supply chain planner110 classifies supply chain networks according to three classifications:convergent, divergent, and generic. Based on the assignedclassification, supply chain planner 110 selects a decomposition processthat is customized for supply chain networks of the assignedclassification. In one embodiment, classifying supply chain networkscomprises modeler 202 of supply chain planner 110 generating a supplychain graph based on the supply chain network model.

FIG. 6 illustrates an exemplary simplified supply chain network 600,according to a further embodiment. Simplified supply chain network 600comprises six material buffers (B1-B6) 602 a-602 f storing items, fiveoperations (O1-O5) 604 a-604 e for processing items, and three resourcebuffers (R1, R2, and R5) 606 a-606 c, which represent capacitylimitations on each of the operations to which they are connected. Fouroperations (O1, O2, O4, O5) 604 a-604 d have a single item as input anda single item as output. A single operation (O3) 604 c requires twoitems as input (i.e. materials or items stored at buffers B3 and B5) andproduces a single item as output (materials or items stored at bufferB4).

By way of example only and not of limitation, supply chain network 600stores raw materials at the most upstream material buffers (B1 and B6)602 a and 602 f. Material buffers (B1 and B6) 602 a and 602 f mayreceive raw materials as the initial input for a manufacturing process.Raw materials may comprise, for example, metal, fabric, adhesives,polymers, and other materials and compounds used during manufacturing.The flow of raw materials from materials buffers (B1 and B6) 602 a and602 f is indicated by edges 608 a and 608 j, which identify operations(O1 and O5) 604 a and 604 e as a possible destination for the rawmaterials. Operations (O1 and O5) 604 a and 604 e may compriseproduction processes that receive raw materials and produce one or moreintermediate items, which are then stored at material buffers (B2 andB5) 602 b and 602 e as indicated by edges 608 b and 608 i. Operations(O1 and O5) 604 a and 604 e are additionally coupled by edges 602 a and602 c with resource buffers (R1 and R5) 606 a and 606 c to indicate thatoperations (O1 and O5) 604 a and 604 e require the resources fromresource buffers (R1 and R5) 606 a and 606 c to process raw materialsstored at the most upstream material buffers (B1 and B6) 602 a and 602 finto intermediate items stored at material buffers (B2 and B5) 602 b and602 e. According to embodiments, resources represented by resourcebuffers (R1 and R5) 606 a and 606 c may include, for example, equipmentor facilities for manufacturing, distribution, or transportation.Although resource buffers (R1 and R5) 606 a and 606 c are described ascomprising equipment or facilities for manufacturing, distribution, ortransportation, embodiments contemplate any number of one or moreresource buffers representing other suitable resources utilized insupply chain operations, according to particular needs.

According to embodiments, edges 608 a-608 j and 610 a-610 c representlimitations on supplying items to particular buffers including, but notlimited to, for example, transportation limitations (such as, forexample, cost, time, available transportation options) or outputs ofvarious operations (such as, for example, different productionprocesses, which produce different items, each of which may berepresented by a different SKU, and which each may be stored atdifferent buffers). For the exemplary simplified supply chain network600, transportation processes may transport, package, or shipintermediate and finished goods to one or more locations internal to orexternal of one or more supply chain entities 150 of supply chainnetwork 100, including, for example, shipping directly to consumers, toregional or strategic distribution centers, or to the inventory of oneor more supply chain entities 150, including, for example, to replenisha safety stock for one or more items in an inventory of one or moresupply chain entities 150. Additionally, particular items and operationsdescribed herein comprise a simplified description for the purpose ofillustration. Items may, for example, comprise different sizes, styles,or states of a same or a different item. Similarly, an operation may beany process or operation, including manufacturing, distribution,transportation, or any other suitable action of the supply chainnetwork. Although the limitation of the flow of items between nodes ofsimplified supply chain network 600 is described as cost, timing,transportation, or production limitations, embodiments contemplate anysuitable flow of items (or limitations of the flow of items) between anyone or more different nodes of simplified supply chain network 600,according to particular needs. In one embodiment, simplified supplychain network 600 includes additional constraints, such as, for example,business constraints, operation constraints, and resource constraints,which facilitate one or more other planning rules. Although simplifiedsupply chain network 600 is shown and described as having a particularnumber and configuration of material buffers 602 a-602 f, operations 604a-604 e, resource buffers 606 a-606 c, and edges 608 a-608 j and 610a-610 c, embodiments contemplate any number of buffers, resources,operations, and edges with any suitable flow between them, according toparticular needs.

FIG. 7 illustrates exemplary supply chain graph 700 representingexemplary simplified supply chain network 600 of FIG. 6, in accordancewith an embodiment; Supply chain graph 700 comprises a modeled graph ofsimplified supply chain network 600. Supply chain graph 700 comprises amodel of nodes and edges, wherein nodes represent material and resourcebuffers and edges represent consuming, producing, or loading an item orresource. According to an embodiment, decomposition module 204 applies amaxflow-mincut process to a supply chain graph to determine partitioningof a supply chain planning problem into balanced subproblems.

Supply chain graph 700 comprises material buffer nodes 602 a-602 f (B1,B2, B3, B4, B5, and B6), which indicate a particular item at aparticular location in supply chain network 100. Material buffer nodes602 a-602 f correspond to storage constraints of material buffer nodes602 a-602 f of simplified supply chain network 600. Resource buffernodes 606 a-606 c (R1, R2, and R5) indicate a resource having aparticular capacity, such as, for example, transportation,manufacturing, or other activities, and correspond to resource buffernodes 606 a-606 c of simplified supply chain network 600. Each of edges702 a-702 h represent consumption, production, or loading of an item orresource from one or more material buffer nodes 602 a-602 f or one ormore resource buffer nodes 606 a-606 c to one or more other materialbuffer nodes 602 a-602 f or resource buffer nodes 606 a-606 c. By way ofexample only and not by way of limitation, edges 702 a-702 b connectingmaterial buffer node B1 602 a and resource buffer node R1 606 a tomaterial buffer node B2 602 b, indicate that a supply of materials frommaterial buffer B1 and a capacity of a resource at resource buffer R1are consumed to generate the material represented by material buffer B2.In addition, each of edges 702 a-702 h comprises a weight correspondingto the number of units of material or resource consumed for each unit ofmaterial produced. Using these weights, decomposition module 204 uses amaxflow-mincut process to identity the minimum number of nodes that, ifremoved, generates decomposed and balanced subproblems. Whendecomposition module 204 cannot partition the supply chain network usingthe maxflow-mincut process, supply chain planner 110 decomposes thesupply chain network using a decomposition process that is customizedaccording to the classification of the supply chain network asconvergent, divergent, or generic, as described in further detail below.

FIG. 8A illustrates exemplary simplified convergent supply chain network802, in accordance with an embodiment. Simplified convergent supplychain network 802 comprises buffer nodes 804, edges 806, andcomplicating constraint nodes 808 a-808 c. Complicating constraint nodes808 a-808 c comprise two complicating material constraint nodes 808a-808 b and a single complicating resource constraint node 808 c. Aconvergent supply chain network such as simplified convergent supplychain network 802 proceeds from a larger number of upstream material andresource buffer nodes 804 to a smaller number of downstream material andresource buffer nodes 804. When a supply chain network has a convergentstructure, complicating constraints are more likely to be located at thedownstream side of the supply chain network (as indicated bycomplicating constraint nodes 808 a-808 c). Accordingly, whendecomposition module 204 searches supply chain network for complicatingconstraints to partition a convergent supply chain network into two ormore subproblems, decomposition module 204 begins searching from themost downstream material and resource buffer nodes 804. As stated above,when decomposing a supply chain network into two or more supply chains,decomposition module 204 replicates complicating constraint nodes 808a-808 c in each of the two or more supply chains sharing thecomplicating material or resource. In the illustrated example,simplified convergent supply chain 802 is decomposed into two supplychains by partitioning simplified convergent supply chain network 802into simplified decomposed convergent supply chains 810 a-810 b andreplicating complicating constraint nodes 808 a-808 c in both supplychains.

FIG. 8B illustrates exemplary simplified divergent supply chain network812, in accordance with an embodiment. In contrast to simplifiedconvergent supply chain network 802, simplified divergent supply chainnetwork 812 comprises a smaller number of upstream material and resourcebuffer nodes 804 becoming a larger number of downstream material andresource buffer nodes 804. When a supply chain network has a divergentstructure, complicating constraints are more likely to be located at theupstream side of the supply chain network (as indicated by complicatingmaterial constraint nodes 808 a-808 b). Accordingly, when decompositionmodule 204 searches supply chain network for complicating constraints topartition a divergent supply chain network into two or more subproblems,decomposition module 204 begins searching from the most upstreammaterial and resource buffer nodes 804. In the example of theillustrated embodiment, simplified divergent supply chain network 812 isdecomposed into two supply chains by partitioning simplified divergentsupply chain network 812 into simplified decomposed divergent supplychains 814 a-814 b and replicating complicating constraint nodes 808a-808 c in both supply chains.

FIG. 8C illustrates exemplary simplified generic supply chain network816, in accordance with an embodiment. When a supply chain network isnot classified as convergent or divergent, supply chain planner 110assigns the supply chain network a generic classification. Simplifiedgeneric supply chain network 816 does not comprise either a largernumber of upstream material and resource buffer nodes 804 becoming asmaller number of downstream material and resource buffer nodes 804 or asmaller number of upstream material and resource buffer nodes 804becoming a larger number of downstream material and resource buffernodes 804. When a supply chain network has a generic structure,complicating constraints are more likely to be located at nodes having agreater number of connections with other nodes in the network (asindicated by complicating material constraint nodes 808 a-808 b, eachhaving four connections with other material and resource buffer nodes804). Accordingly, when decomposition module 204 searches supply chainnetwork for complicating constraints to partition a generic supply chainnetwork into two or more subproblems, decomposition module 204 beginssearching from the material and resource buffer nodes 804 having themost connections with other material and resource buffer nodes 804.Although the decomposition of simplified generic supply chain network816 is not illustrated, embodiments contemplate partitioning andreplicating complicating constraint nodes 808 a-808 c into any number oftwo or more decomposed supply chains, according to particular needs.

FIG. 9 illustrates method 900 of solving a supply chain planning problemby functional decomposition, in accordance with an embodiment. Exemplarymethod 900 proceeds by one or more activities, which although describedin a particular order may be performed in one or more permutations,according to particular needs.

Method 900 begins at activity 902, where modeler 202 of supply chainplanner 110 models a supply chain planning problem of supply chainnetwork 100 as a linear programming LP problem. As stated above, modeler202 generates an LP master supply chain planning problem by modeling thematerials, resources, operations, constraints, and objectives of supplychain network 100.

At activity 904, decomposition module 204 checks for prioridentification of complicating nodes. To identify previously identifiedcomplicating nodes, decomposition module 204 may check data models 210for previous decomposed supply chains and/or check current andhistorical supply chain data for underutilized or flexible constraints.As disclosed above, supply chain planner 110 decomposes an LP mastersupply chain planning problem by partitioning the supply chain networkof the modeled supply chain planning problem at one or more complicatingnodes. According to embodiments, decomposition module 204 checks datamodels 210 for previously identified complicating nodes to determinewhether material or resource buffers for the supply chain network of thecurrent supply chain planning problem have been previously identified ascomplicating constraints. In addition, or as an alternative,decomposition module 204 analyzes results of previous supply chainplanning solves, which may be stored as LP formulations 214, to searchresources for capacities that are not fully used and material buffershaving above-average unused remaining inventory. Embodiments ofdecomposition module 204 further contemplate searching alternate oradditional sources of materials and capacity to identify flexibleconstraints. Material constraints may be flexed by, for example,locating a cheap alternate supplier of an item or subcontractingproduction, operation, or transportation to increase materialavailability and storage at one or more constrained buffers. Resourceconstraints may be flexed by, for example, using overtime labor toincrease availability of one or more resources, such as, for example,operating or repairing production machinery. Although particularexamples of flexible constraints are described, embodiments contemplateflexing any material or resource constraint using any suitable method orsource, according to particular needs.

At activity 906, decomposition module 204 partitions the supply chainnetwork of the modeled supply chain planning problem based, at least inpart, on the complicating constraints identified at activity 904.According to one embodiment, decomposition module 204 partitions thesupply chain network of the modeled supply chain planning problem at oneor more of the complicating constraints that are identified ascomplicating nodes.

At activity 908, decomposition module 204 determines whether theresulting decomposed supply chains generate balanced subproblems.Decomposition module 204 may determine subproblems are balanced bychecking the subproblems have equal sizes. According to embodiments, thesize of as subproblem is the number of its constraints. In manyinstances, decomposition module 204 cannot decompose the multi-periodsupply chain planning problem into two subproblems having equal size andcomplexity. When the subproblems do not have equal size, decompositionmodule 204 may determine the decomposed subproblems are balanced byevaluating the difference in the size of the subproblems is minimized.For example, when decomposition module 204 determines that the divisionof the multi-objective supply chain planning problem is not equal,decomposition module 204 selects the functional decomposition thatprovides subproblems having similar sizes by checking that thedifference between the number of constraints of each subproblem isminimized.

When the previously identified complicating constraints partition supplychain network into smaller supply chains that generate balancedsubproblems, functional decomposition method 900 continues to activity918, where decomposition module 204 identifies a variable partition.

When the previously-identified complicating constraints cannot be usedto partition supply chain network into balanced subproblems, method 900continues to activity 910, where decomposition module 204 analyzes asupply chain network graph to determine whether the supply chain networkof the modeled supply chain planning problem is convergent or divergent.When the supply chain network is identified as convergent or divergent,method 900 continues to activity 912, where decomposition module 204partitions the supply chain network. As described in further detailbelow, decomposition module 204 partitions the supply chain networkusing method 1000 (FIG. 10) with a modification that improvespartitioning speed of convergent or divergent supply chain networks.

At activity 914, decomposition module 204 determines whether theresulting decomposed supply chains generate balanced subproblems. Asdisclosed above, decomposition module 204 may check whether thesubproblems are balanced by evaluating the difference in the number ofconstraints between the subproblems.

When the supply chain network cannot be identified as convergent ordivergent at activity 910 or when solver 206 determines that thesubproblems are not balanced at activity 914, method 900 continues toactivity 916, where solver 206 partitions the supply chain network usingmethod 1000 with a modification for generic supply chain networks.According to embodiments, generic supply chain networks are supply chainnetworks that decomposition module 204 is unable to classify asconvergent or divergent.

In response to decomposition module 204 determining that the subproblemsare balanced at activity 914 or after partitioning the subproblems usingthe generic modification of method 1000 at activity 916, method 900continues to activity 918, where decomposition module 204 partitions thevariables of the subproblems by removing variables from each of thesubproblems, which were present in the master supply chain planningproblem (before decomposition), but which are not present in thedecomposed subproblem.

At activity 920, supply chain planner partitions the constraints of thesubproblems. In one embodiment, decomposition module 204 partitions theconstraints by generating a list of the constraints of the master supplychain planning problem for each of the decomposed subproblems.

At activity 922, modeler 202 formulates LP subproblems based on thevariable and constraint partitions that decomposition module 204identifies from the functional decomposition. Modeler 202 formulatesfunctional-based decomposed subproblems by dividing the original LPsupply chain master planning problem at one or more decompositionboundaries and generating at least two independent subproblems sharing,as their common element, one or more complicating constraints. Accordingto one embodiment, each of functional-based decomposed subproblemscomprise LP optimization problems and may be represented by a matrixformulation and solved at the matrix level.

At activity 924, solver 206 performs masterless iteration withsubgradient descent. According to embodiments, solver 206 calculates theeffective dual for the decomposed subproblems, updates the subproblems,monitors for convergence or one or more stopping criteria, anditeratively updates the LP problems until one or more stopping criteriaare detected, as described in further detail below. When the masterlessiteration with subgradient converges or one or more stopping criteriaare detected, solver 206 generates the solution to the master LP supplychain planning problem at activity 926.

At activity 928, supply chain solver 206 checks whether the currentobjective of the LP subproblems is the final objective. When supplychain solver 206 determines the current objective is not the finalobjective, method 900 continues to activity 930, where solver 206calculates the reduced cost and updates the upper and lower bounds forthe current objective level to preserve the optimality of the generatedsolution, accesses the objective formulation for the next objective, andreturns to activity 922, where solver 206 updates and iteratively solvesthe functional-based decomposed subproblems for the new objective.According to one embodiment, solver 206 of supply chain planner 110solves functional-based decomposed subproblems by iteratively loadingand solving the functional-based decomposed subproblems for eachobjective in accordance with an order described by a hierarchy of theobjectives. A hierarchy of the objectives indicates that thehierarchical objectives are solved in the order indicated by thehierarchy, from an objective higher in the hierarchy (higher order orhigher priority objective) to an objective lower in the hierarchy (lowerorder or lower priority objective). According to embodiments, thehierarchical order of the objectives indicates the order of importanceof the objectives (such as, for example, the first objective is moreimportant than the second objective; the second objective is moreimportant than the third objective, etc.). When solving thefunctional-based decomposed subproblems for one or more lowerobjectives, solver 206 sets decision variables at their upper or lowerbounds (which may be referred to as variable fixing) to retain theobjective value of one or more higher objectives. After solving thefunctional-based decomposed subproblems for a current objective andusing masterless iteration with subgradient descent to generate aglobally-optimal LP-solution, solver 206 updates variables to be fixedat their upper or lower bounds. Solver 206 may then iteratively repeatsolving the functional-based decomposed subproblems for each objectiveuntil solver 206 solves all objectives of the multi-objectivehierarchical LP supply chain master planning problem or solver 206detects one or more stopping criteria, as described below.

During variable fixing, solver 206 fixes particular variables to theirupper or lower bounds according to a list that is updated after eachobjective solve. Generally, variables which can deteriorate an objectivevalue are fixed at their lower bounds, variables which can improve anobjective value are fixed at their upper bounds, and variables which areneutral remain unfixed. Upon solving the LP problem, solver 206generates, as part of the solution data, a reduced cost of eachvariable. In the case of a minimization objective, when a variable has apositive reduced cost, then it will deteriorate the objective and hencebe fixed to its lower bound, while a variable with a negative reducedcost will improve the objective value and hence be fixed to its upperbound.

Returning to activity 928, when supply chain solver 206 detects that thecurrently solved objective is the final objective, method 900 ends.

FIG. 10 illustrates method 1000 of supply chain network partitioning, inaccordance with an embodiment. Exemplary method 1000 proceeds by one ormore activities, which although described in a particular order may beperformed in one or more permutations, according to particular needs.

At activity 1002, modeler 202 models the supply chain network of thesupply chain planning problem as a supply chain graph. As disclosedabove, modeler 202 generates a supply chain graph, such as, for example,supply chain graph 700, comprising a network of nodes representingmaterial and resource buffers and edges comprising a weightcorresponding to the number of units of material or resource consumedfor each unit that is processed.

At activity 1004, decomposition module 204 applies a maxflow-mincutprocess to supply chain graph. In one embodiment, decomposition module204 begins the maxflow-mincut process by checking for complicatingnodes. In addition, or as an alternative, when the supply chain networkis determined to be convergent or divergent, the maxflow-mincut processtraverses the supply chain network beginning at the most downstreamnodes and moving upstream or at the most upstream nodes and movingdownstream, respectively. While traversing the supply chain network,decomposition module 204 searches for complicating edges which producebalanced subproblems and minimize complicating constraints. According toone embodiment, decomposition module 204 uses depth first search (DFS)to traverse the supply chain network. According to an embodiment,decomposition module 204 uses a maxflow-mincut process to identity theminimum number of nodes that, if removed, generates decomposed andbalanced subproblems.

At activity 1006, decomposition module 204 checks for balancedsubproblems. When decomposition module 204 detects balanced subproblems,method 1000 continues to activity 1008, where decomposition module 204generates connected component list and complicating links. The connectedcomponent list comprises a list of nodes in the supply chain networkwhich are separated from the other components, and may include, forexample, a list of nodes, variables, and constraints for each of thedecomposed subproblems. Complicating links are edges between the nodeswhich, when removed, decompose the supply chain planning problem.Constraints of the complicating links may be referred to as complicatingentities.

When decomposition module 204 does not detect balanced subproblems,method 1000 continues to activity 1010, where decomposition module 204traverses the material and resource nodes of the supply chain networkusing a decomposition process that is customized according to theclassification of the supply chain network as convergent, divergent, orgeneric, as stated above. According to one embodiment, decompositionmodule 204 assigns levels to each resource and material buffer accordingto their placement in the supply chain network and/or their number ofconnected edges.

At activity 1012, decomposition module 204 determines which of thetraversed buffers has two or more consuming flows, producing flows, orloading operations.

At activity 1014, decomposition module 204 determines the complicatingconnecting entity having the lowest assigned level, and decompositionmodule 204 removes the resource load or material flow from eachcomplicating entity, at activity 1016. At activity 1018, decompositionmodule 204 searches for connected entities using depth-first-search forconnected nodes for each operation in each of the removed resource loadsand material flows.

At activity 1020, decomposition module 204 checks for common connectedentities. When decomposition module 204 detects common connectedentities, method 1000 returns to activity 1014, and decomposition module204 iteratively processes each common connected node as a complicatingentity until decomposition module 204 does not detect common connectedentities at activity 1020. When decomposition module 204 does not detectcommon connected entities, method 1000 continues to activity 1022, wheredecomposition module 204 provides the collected composed supply chains(comprising, for example, the connected component list and thecomplicating links) to modeler 202 to generate decomposed supply chainsubproblems based on the identified connected components andcomplicating links.

As disclosed above, method 1000 is modified at activity 1010 based, atleast in part, on the classification of the supply chain network asconvergent, divergent, or generic.

FIG. 11A illustrates exemplary code implementing the dividing of aconvergent supply chain at a complicating node, in accordance with anembodiment. When the decomposition module 204 determines the supplychain network is convergent, decomposition module 204 traverses thesupply chain network from the most downstream buffers and proceedstoward the upstream buffers.

FIG. 11B illustrates exemplary code implementing the dividing of adivergent supply chain at a complicating node, in accordance with anembodiment. When the supply chain network is divergent, decompositionmodule 204 traverses the supply chain network from the most upstreambuffers and proceeds toward the downstream buffers, searching forcomplicating nodes that provide for partitioning the supply chainnetwork into balanced supply chains. FIG. 11C illustrates exemplary codeimplementing the dividing of a generic supply chain at a complicatingnode, in accordance with an embodiment. When the supply chain network isnot convergent or divergent, decomposition module 204 traverses thesupply chain network according to the number of edges connecting to eachof the nodes. For a generic supply chain network, decomposition module204 proceeds from the node having the most connections to the nodehaving the least number of connections.

FIG. 12 illustrates method 1200 of solving functional-based decomposedsubproblems using masterless iteration, in accordance with anembodiment. Exemplary method 1200 proceeds by one or more activities,which although described in a particular order may be performed in oneor more permutations, according to particular needs.

As disclosed above, method 1200 of functional decomposition generates aglobally-optimal LP solution to a LP supply chain master planningproblem using masterless iteration with subgradient descent to solvedecomposed subproblems. Although method 1200 is shown and described asgenerating the globally-optimal LP solution by solving functional-baseddecomposed subproblems using a heuristic solver and applying masterlessiteration with subgradient descent, embodiments contemplate generatingglobally-optimal LP solution and solving functional-based decomposedsubproblems using any one or more optimization or heuristic solvers,other methods of masterless iteration, and/or other subgradient methods,according to particular needs. In addition, although method 1200 isdescribed as generating the globally-optimal LP solution, embodimentscontemplate infeasible problems, wherein method 1200 stops. Thisinfeasibility may however be translated to functional information toidentify a new stopping criteria or provide additional insight into thesupply chain planning problem for supply chain planner 110. Embodimentsalso contemplate, when the solution to the subproblems is infeasible,adding virtual variables that make the problem feasible, and, later,removing them.

Method 1200 begins at activity 1202 where solver 206 accessesfunctional-based decomposed subproblems (Subproblem 1, Subproblem 2 . .. Subproblem n). As discussed above, each of functional-based decomposedsubproblems comprise LP formulations of the LP supply chain masterplanning problem split at one or more nodes representing one or morecomplicating constraints and sharing the material or capacity for theone or more complicating constraints.

At activity 1204, solver 206 checks whether the current iteration is thefirst iteration (i.e. when the iteration number is equal to one). Whenthe current iteration is the first iteration, solver 206 continues toactivity 1206 and initializes the Right Hand Side (RHS) and/or thevariable bounds of the functional-based decomposed subproblems.According to embodiments, the sum of the RHS of the functional-baseddecomposed subproblems equals the RHS of the master LP problem, andsolver 206 allocates all of the capacity or material of the complicatingconstraint of the LP supply chain master planning problem to thecomplicating constraints of the functional-based decomposed subproblems,such that the total capacity and material allocated to functional-baseddecomposed subproblems equals 100% of the capacity or material of the LPsupply chain master problem.

At activity 1208, solver 206 solves the functional-based decomposedsubproblems. According to one embodiment, solver 206 solves thefunctional-based decomposed subproblems using LP optimization.

Each of the functional-based decomposed subproblems comprises its ownobjectives, constraints, and variables. As stated previously,complicating constraints are common to functional-based decomposedsubproblems. Each of the complicating constraints is split on the RHS ofthe subproblems, and when solver 206 solves functional-based decomposedsubproblems, a dual value is calculated for each of the constraints infunctional-based decomposed subproblems.

At activity 1210, solver 206 uses the calculated duals to calculate aneffective dual. According to the embodiments, the effective dual is themathematical difference of the dual values of the complicatingconstraints of the functional-based decomposed subproblems. By way ofexample only and not of limitation, when a dual value of a firstsubproblem equals one hundred and a dual value of a second subproblemequals twenty-five, solver 206 calculates the effective dual asseventy-five. Although the effective dual is described as a differenceof two dual values for a single complicating constraint, embodimentscontemplate calculating effective duals for any number of subproblemsand any number of complicating constraints, according to particularneeds.

At activity 1212, solver 206 combines the solutions of each of thesubproblems for the current objective. The final solution (objectivevalues) is the sum of objective values (solutions) of each of thesubproblems.

At activity 1214, solver 206 checks for one or more stopping criteria.According to an embodiment, one or more stopping criteria may comprisedetecting the globally-optimal LP solution. In this embodiment, method1200 ends when solver 206 determines that the current solution is theglobally-optimal LP solution. Additionally, or in the alternative,stopping criteria may comprise one or more of a functionalinterpretation of supply chain data 210, infeasibility in capacity ormaterial less than, equal to, and/or greater than a predetermined value,percentage, or other threshold. [e.g. a material is over or underconsumed, or a capacity is over or under consumed.] In one embodiment,stopping criteria comprise functional interpretation of dual values. Inaddition, or as an alternative, stopping criteria comprise detecting avalue of infeasibility and/or suboptimality for the solve exceeds athreshold while solving the functional-based decomposed subproblems.According to an embodiment, one or more thresholds are preselectedtolerance for infeasibility and suboptimality which, when met orexceeded, method 1200 ends. By way of example only and not oflimitation, supply chain planner 110 sets a threshold for a resource at0.01%. When solver 206 determines a solution utilizes the resource±1% ofthe actual availability and finds a converging point, solver 206 stopscorrecting the 1% infeasibility. According to embodiments, theinfeasibility tolerance comprises a threshold value for one or morematerial flows. In addition or as an alternative, stopping criteriacomprise a suboptimality tolerance that sets a minimum differencebetween the objective value of iterative solutions. For example, whensolver 206 detects that a difference between a number of previousconsecutive iterations of the objective value (such as, for example,five iterations) is less than a preselected suboptimality threshold,solver 206 ends the solve. Although the stopping criteria are describedas particular values of infeasibility and suboptimality thresholds,embodiments contemplate the infeasibility and suboptimality tolerancescomprising any threshold, according to particular needs.

According to an embodiment, decomposition module 204 decomposes a supplychain planning problem that is formulated as one or more LP matricesusing functional decomposition. In this exemplary embodiment,decomposition module 204 decomposes the supply chain planning probleminto two or more submatrices. Solver 206 solves the subproblemsrepresented by the two or more submatrices using a CPLEX solver andaccording to a combined objective. Solver 206 checks for one or morestopping criteria. When the one or more stopping criteria are notdetected, method 1200 continues the masterless iteration withsubgradient descent to generate two or more new supply chain planningproblems represented by the two or more submatrices.

At activity 1216, solver 206 updates the learning rate. According to anembodiment, solver 206 updates the learning rate during each iterationby dividing the previous learning rate by a factor calculated as thesquare root of the current iteration number. For the first iteration,the learning rate does not change because the factor for the firstiteration is equal to one (i.e. the square root of one is one). At asecond iteration, the learning rate may be calculated by dividing thelearning rate of the first iteration by the square root of two.Similarly, for the third iteration, the learning rate may be calculatedby dividing the learning rate of the second iteration by the square rootof three; the fourth iteration's learning rate is the learning rate ofthe third iteration divided by two (i.e. the square root of four); andso on.

After increasing the iteration counter by one at activity 1218, method1200 returns to activity 1204, where solver 206 again evaluates thecurrent iteration number. The current iteration is the second iteration,iteration number is equal to two, and method 1200 continues to activity1220, where solver 206 updates the RHS bounds using the currenteffective dual and learning rate. As stated above, modeler 202initializes the RHS of the functional-based decomposed subproblems sothat the sum of the RHS of the functional-based decomposed subproblemsis equal to the RHS of the master LP problem. According to oneembodiment, solver 206 updates the RHS of the complicating constraints,iteratively, and tries to find the best assignment using dual values, toachieve the optimum solution. Solver 206 may iteratively loop throughthe masterless iteration method for each hierarchical objective leveland fixing variables to upper and lower bounds until detecting one ormore other stopping criteria, such as, for example, determining that thecurrent solution is the globally-optimal LP solution, as disclosedabove.

Because the functional decomposition method provides for speeding up therun time for generating globally-optimal LP solutions to supply chainmaster planning problems, batch runs (e.g., daily, weekly, and the like)may be performed in less time, and changes in customer data may beaddressed sooner (e.g., an intraday rerun of optimization based onchanges in data).

FIGS. 13A-13B illustrate chart 1302 and table 1304 comparing run time ofan LP optimization method (LPOPT) and functional decomposition method900, according to an embodiment. Chart 1302 illustrates run time 1306for five test cases 1308 a-1308 e for three solving methods: LPOPT 1310;functional decomposition method 900 solving the subproblems sequentially(Functional Sequential) 1312; and functional decomposition method 900solving the subproblems in parallel (Functional Parallel) 1314.Functional decomposition method 900 generates the same optimal solutionfor the highest objective level and deviates from a perfectly optimalsolution on lower objectives by less than one percent, which isnegligible. Results for test cases 1308 a-1308 e comprise Number ofComplicating Constraints/Total Constraints 1320, run time 1322 for LPOPT1310, run times 1324 for Functional Sequential 1312, run times 1326 forFunctional Parallel, percentage 1328 of runtime (Functional Sequential1312/LPOPT 1310), and percentage 1330 of runtime (Functional Parallel1314/LPOPT 1310). Run times 1324 for Functional Sequential 1312 (78 s.;141 s.; 30 s.; 79.81 s.; 69 s.) and run times 1326 for FunctionalParallel 1314 (70 s.; 73 s.; 25.53 s.; 70 s.; 59.9 s.) are significantlyimproved over run times 1322 for LPOPT 1310 (98 s.; 185.63 s.; 40.65 s.;101.28 s.; 127 s.), and Functional Parallel 1314 was the fastestoverall. Test sets 1308 a-1308 e validate the performance benefit forusing functional-based decomposition method 900 over LPOPT 1310,providing speed improvements between 28.57% and 60.67%.

Reference in the foregoing specification to “one embodiment”, “anembodiment”, or “some embodiments” means that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the invention. The appearancesof the phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

While the exemplary embodiments have been shown and described, it willbe understood that various changes and modifications to the foregoingembodiments may become apparent to those skilled in the art withoutdeparting from the spirit and scope of the present invention.

What is claimed is:
 1. A system of solving a supply chain planningproblem modeled as a linear programming (LP) problem, comprising: acomputer, comprising a processor and memory, the computer configured to:receive an LP problem representing a supply chain planning problem for asupply chain network, the supply chain network comprising materialbuffers and resource buffers; partition the supply chain network at acomplicating node into at least two supply chains sharing thecomplicating node; formulate a decomposed subproblem for each of the atleast two supply chains; generate a globally-optimal LP solution to theLP problem using subgradient descent with an effective dual by:calculate the effective dual based, at least in part, on a mathematicaldifference of at least two dual values, each of the at least two dualvalues calculated by solving the functional-based decomposedsubproblems; combine solutions for the solved functional-baseddecomposed subproblems; and repeating the calculate and combine stepsuntil a stopping criteria is met indicating a threshold of infeasibilityor a threshold suboptimality; and generate a globally-optimal LPsolution to the LP problem based on the stopping criteria indicatingoptimality of the combined solutions.
 2. The system of claim 1, whereinthe computer is further configured to: generate a supply chain graph bymodeling the material buffers and resource buffers as nodes andconsumption, production, or loading as weights on edges connecting thenodes; and search for balanced supply chains using a maxflow.mincuttechnique.
 3. The system of claim 2, wherein the computer is furtherconfigured to: in response to not detecting balanced supply chains usingthe maxflow.mincut technique, traverse the material buffers and resourcebuffers to locate the complicating node.
 4. The system of claim 3,wherein traverse the material buffers and resource buffers to locate thecomplicating node comprises moving from upstream nodes to downstreamnodes in response to determining the supply chain network is divergent.5. The system of claim 3, wherein traverse the material buffers andresource buffers to locate the complicating node comprises moving fromdownstream nodes to upstream nodes in response to determining the supplychain network is convergent.
 6. The system of claim 3, wherein traversethe material buffers and resource buffers to locate the complicatingnode comprises moving from most connected nodes to least connectednodes.
 7. The system of claim 3, wherein the computer is furtherconfigured to: update a learning rate and a value of the complicatingnode based, at least in part, on the calculated effective dual; andsolve the functional-based decomposed subproblems based, at least inpart, on the updated learning rate and the updated value of thecomplicating node.
 8. A computer-implemented method, comprising:receiving an LP problem representing a supply chain planning problem fora supply chain network, the supply chain network comprising materialbuffers and resource buffers; partitioning the supply chain network at acomplicating node into at least two supply chains sharing thecomplicating node; formulating a decomposed subproblem for each of theat least two supply chains; generating a globally-optimal LP solution tothe LP problem using subgradient descent with an effective dual by:calculating the effective dual based, at least in part, on amathematical difference of at least two dual values, each of the atleast two dual values calculated by solving the functional-baseddecomposed subproblems; combining solutions for the solvedfunctional-based decomposed subproblems; and repeating the calculatingand combining steps until a stopping criteria is met indicating athreshold of infeasibility or a threshold suboptimality; and generatinga globally-optimal LP solution to the LP problem based on the stoppingcriteria indicating optimality of the combined solutions.
 9. The methodof claim 8, further comprising: generating a supply chain graph bymodeling the material buffers and resource buffers as nodes andconsumption, production, or loading as weights on edges connecting thenodes; and searching for balanced supply chains using a maxflow.mincuttechnique.
 10. The method of claim 9, further comprising: in response tonot detecting balanced supply chains using the maxflow.mincut technique,traversing the material buffers and resource buffers to locate thecomplicating node.
 11. The method of claim 10, wherein traversing thematerial buffers and resource buffers to locate the complicating nodecomprises moving from upstream nodes to downstream nodes in response todetermining the supply chain network is divergent.
 12. The method ofclaim 10, wherein traversing the material buffers and resource buffersto locate the complicating node comprises moving from downstream nodesto upstream nodes in response to determining the supply chain network isconvergent.
 13. The method of claim 10, wherein traversing the materialbuffers and resource buffers to locate the complicating node comprisesmoving from most connected nodes to least connected nodes.
 14. Themethod of claim 10, further comprising: updating a learning rate and avalue of the complicating node based, at least in part, on thecalculated effective dual; and solving the functional-based decomposedsubproblems based, at least in part, on the updated learning rate andthe updated value of the complicating node.
 15. A non-transitorycomputer-readable medium embodied with software, the software whenexecuted: receives an LP problem representing a supply chain planningproblem for a supply chain network, the supply chain network comprisingmaterial buffers and resource buffers; partitions the supply chainnetwork at a complicating node into at least two supply chains sharingthe complicating node; formulates a decomposed subproblem for each ofthe at least two supply chains; generates a globally-optimal LP solutionto the LP problem using subgradient descent with an effective dual by:calculates an effective dual based, at least in part, on a mathematicaldifference of at least two dual values, each of the at least two dualvalues calculated by solving the functional-based decomposedsubproblems; combines solutions for the solved functional-baseddecomposed subproblems; and repeating the calculates and combines stepsuntil a stopping criteria is met indicating a threshold of infeasibilityor a threshold suboptimality; and generates a globally-optimal LPsolution to the LP problem based on the stopping criteria indicatingoptimality of the combined solutions.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the software when executedfurther: generates a supply chain graph by modeling the material buffersand resource buffers as nodes and consumption, production, or loading asweights on edges connecting the nodes; and searches for balanced supplychains using a maxflow.mincut technique.
 17. The non-transitorycomputer-readable medium of claim 16, wherein the software when executedfurther: in response to not detecting balanced supply chains using themaxflow.mincut technique, traverses the material buffers and resourcebuffers to locate the complicating node.
 18. The non-transitorycomputer-readable medium of claim 17, wherein traverses the materialbuffers and resource buffers to locate the complicating node comprisesmoving from upstream nodes to downstream nodes in response todetermining the supply chain network is divergent.
 19. Thenon-transitory computer-readable medium of claim 17, wherein traversesthe material buffers and resource buffers to locate the complicatingnode comprises moving from downstream nodes to upstream nodes inresponse to determining the supply chain network is convergent.
 20. Thenon-transitory computer-readable medium of claim 17, wherein traversesthe material buffers and resource buffers to locate the complicatingnode comprises moving from most connected nodes to least connectednodes.